Evaluate n → ∞ lim ∫ 0 1 ∫ 0 1 … ∫ 0 1 x 1 + x 2 + ⋯ + x n n d x 1 d x 2 … d x n .
Clarification: In the answer options, e ( ≈ 2 . 7 1 8 2 8 ) is the Euler's number .
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Your proof needs a little more detail. At about the third line, you state that n → ∞ lim ∫ 0 ∞ ( t / n 1 − e − t / n ) n d t = ∫ 0 ∞ n → ∞ lim ( t / n 1 − e − t / n ) n d t It is not necessarily true that the limit of a sequence of integrals is the integral of the limit function - consider for example the integrals ∫ 0 1 n x n d x ; the integrand tends to 0 for 0 < x < 1 , but the integrals tend to 1 .
What makes this argument work is that ( t / n sinh t / n ) n tends monotonically (decreasingly) to 1 as n → ∞ for all t > 0 . We can then use the Monotone Convergence Theorem to deduce that the limit of the integrals is the integral of the limit.
I know that lim k → 0 k x sinh ( k x ) = 1 , but since this is to the n -th power and n is approaching infinity, the explanation is not so simple.
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I have edited the solution to justify for said limit :)
When I got to what is your fifth line I tried using binomial theorem and failed. Do you think this way would have worked?
As n gets large, most of the region of integration (a high-dimensional cube) is close to the hyperplane joining the vertices where the sum of the xi is n/2 (if n is even) or (n+1)/2 (if n is odd). The integrand is 2 (or tends to 2) on this region so the integral tends to 2×(volume of the hypercube) ie 2.
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This is a heuristic argument. The limit of 2 is reasonable, but analysis requires putting definite controls on quantities like "most of" and the like.
"Most of" the reciprocals 1 / n are very small, but the sum of all of them diverges, for example.
Let { X i } i be a sequence of i.i.d. random variables each following a uniform distribution in the interval [ 0 , 1 ] , with expectation μ = 1 / 2 . Then by the Strong Law of Large Numbers , we have almost surely n → ∞ lim X 1 + X 2 + … + X n n → μ 1 . Then, assuming that the limit and expectation can be exchanged (usually proved by showing that the random sequence is Uniformly Integrable, which I skip), we have n lim E ( X 1 + X 2 + … + X n n ) = E ( lim X 1 + X 2 + … + X n n ) = μ 1 = 2 . The proof follows after noting that the LHS is simply the integral in question. ■
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This may not be the first time for you to see this solution.
First, we note of this integral
∫ 0 ∞ e − a x d x = a 1
We can actually use this to our advantage, to transform the integrand in question, to an integral wrt to a dummy variable t , that is
x 1 + x 2 + x 3 + ⋯ + x n n = ∫ 0 ∞ e − n x 1 + x 2 + ⋯ + x n t d t
Plugging this into the equation, we will get
∫ 0 1 ⋯ ∫ 0 1 d x 1 d x 2 d x 3 … d x n ∫ 0 ∞ e − n x 1 + x 2 + ⋯ + x n t d t
And changing the order of integration, we'll get this
n → ∞ lim ∫ 0 ∞ ( ∫ 0 1 e − n x t d x ) n d t
n → ∞ lim ∫ 0 ∞ ( t / n 1 − e − t / n ) n d t
∫ 0 ∞ n → ∞ lim ( t / n 1 − e − t / n ) n d t
∫ 0 ∞ e − t / 2 n → ∞ lim ( t / n e t / 2 n + e − t / 2 n ) n d t
∫ 0 ∞ e − t / 2 n → ∞ lim ( t / n 2 sinh ( t / 2 n ) ) n d t
∫ 0 ∞ e − t / 2 n → ∞ lim ( t / 2 n sinh ( t / 2 n ) ) n d t
Now, note that lim k → 0 k x sinh k x = 1
This simplifies our integral to
∫ 0 ∞ e − t / 2 d t = 2 . (See edit below)
Edit: to justify for the said limit, we will isolate it first and solve it by means of L'Hopital's rule. First we let u = 2 t .
A = n → ∞ lim [ u / n sinh ( u / n ) ] n
Since A is of the indeterminate form 1 ∞ , we need to transform it to a form where L'Hopital's rule can be used.
ln A = n → ∞ lim n ln [ u / n sinh ( u / n ) ]
This is now of the form 0 ⋅ ∞ . A little more modification will lead us to
ln A = n → ∞ lim n 1 ln ( u / n sinh ( u / n ) )
This can now be evaluated via L'hopital's rule. Differentiating both the numerator and the denominator twice wrt n , we will get
ln A = n → ∞ lim n 4 n 3 [ u 2 csch 2 ( u / n ) − 2 u n coth ( u / n ) + n 2 ]
This will evaluate to zero.
Going back now, we can say that A = e 0 = 1 .